Exploring the Factors Influencing AI Integration in Clinical Diagnostic Decision-Making

Abstract

This study aimed to explore the key factors influencing the integration of artificial intelligence (AI) into clinical diagnostic decision-making from the perspective of healthcare professionals. This research employed a qualitative design based on semi-structured interviews with 23 healthcare professionals in Canada, including physicians, radiologists, clinical informaticians, nurse practitioners, and administrators. Participants were selected through purposive sampling to ensure diverse perspectives, and data collection continued until theoretical saturation was achieved. Interviews were transcribed verbatim and analyzed thematically using NVivo software, with codes and themes developed iteratively through inductive analysis and constant comparison. Four major themes emerged from the data: (1) technological infrastructure and readiness, (2) human and professional factors, (3) organizational culture and leadership, and (4) perceived value and impact of AI. Participants reported that outdated systems, poor interoperability, and insufficient technical support limited integration. Attitudes toward AI varied, with concerns about trust, autonomy, and training gaps. Organizational barriers included lack of leadership strategy and unclear implementation policies. While AI was recognized for enhancing diagnostic accuracy and efficiency, concerns about alert fatigue, liability, and ethical issues were prevalent. Patient trust, professional identity, and collaborative workflows also influenced AI adoption outcomes. Integrating AI into clinical diagnostics is a complex, multidimensional process shaped by technological, professional, organizational, and ethical factors. Beyond technical improvements, successful implementation requires a holistic, sociotechnical approach that addresses infrastructure, education, workflow design, and patient-clinician communication. Institutional strategies should prioritize clinician engagement, interdisciplinary collaboration, and transparent governance to foster responsible and effective AI adoption in healthcare settings.